Overview

Dataset statistics

Number of variables36
Number of observations777
Missing cells52
Missing cells (%)0.2%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory218.7 KiB
Average record size in memory288.2 B

Variable types

NUM18
CAT18

Warnings

TempIL is highly correlated with TempGLHigh correlation
TempGL is highly correlated with TempILHigh correlation
Lich2 is highly correlated with Lich1High correlation
Lich1 is highly correlated with Lich2High correlation
SpatGL is highly correlated with SpatDistHigh correlation
SpatDist is highly correlated with SpatGLHigh correlation
Fstf has 52 (6.7%) missing values Missing
df_index has unique values Unique
UArt1 has 24 (3.1%) zeros Zeros
AUrs1 has 663 (85.3%) zeros Zeros
AUrs2 has 769 (99.0%) zeros Zeros

Reproduction

Analysis started2020-10-29 21:20:03.938380
Analysis finished2020-10-29 21:21:10.962183
Duration1 minute and 7.02 seconds
Software versionpandas-profiling v2.9.0
Download configurationconfig.yaml

Variables

df_index
Real number (ℝ≥0)

UNIQUE

Distinct777
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean915.8996139
Minimum1
Maximum1866
Zeros0
Zeros (%)0.0%
Memory size6.1 KiB
2020-10-29T22:21:11.318339image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile95
Q1442
median888
Q31406
95-th percentile1787.2
Maximum1866
Range1865
Interquartile range (IQR)964

Descriptive statistics

Standard deviation550.9675746
Coefficient of variation (CV)0.6015589113
Kurtosis-1.256213593
Mean915.8996139
Median Absolute Deviation (MAD)480
Skewness0.08576073524
Sum711654
Variance303565.2683
MonotocityStrictly increasing
2020-10-29T22:21:11.462584image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
106610.1%
 
31410.1%
 
34410.1%
 
34310.1%
 
129610.1%
 
34110.1%
 
34010.1%
 
136210.1%
 
33710.1%
 
33410.1%
 
Other values (767)76798.7%
 
ValueCountFrequency (%) 
110.1%
 
710.1%
 
1210.1%
 
1610.1%
 
1710.1%
 
ValueCountFrequency (%) 
186610.1%
 
186510.1%
 
186210.1%
 
186110.1%
 
186010.1%
 

TempMax
Real number (ℝ≥0)

Distinct132
Distinct (%)17.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean129.7027027
Minimum9
Maximum1341
Zeros0
Zeros (%)0.0%
Memory size6.1 KiB
2020-10-29T22:21:11.616249image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum9
5-th percentile21
Q160
median96
Q3147
95-th percentile357.6
Maximum1341
Range1332
Interquartile range (IQR)87

Descriptive statistics

Standard deviation126.7814212
Coefficient of variation (CV)0.9774770964
Kurtosis24.14024112
Mean129.7027027
Median Absolute Deviation (MAD)39
Skewness3.888181057
Sum100779
Variance16073.52877
MonotocityNot monotonic
2020-10-29T22:21:11.766916image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
81222.8%
 
96212.7%
 
111202.6%
 
87202.6%
 
93192.4%
 
48192.4%
 
84192.4%
 
54182.3%
 
63182.3%
 
60182.3%
 
Other values (122)58375.0%
 
ValueCountFrequency (%) 
940.5%
 
1260.8%
 
1570.9%
 
18141.8%
 
21101.3%
 
ValueCountFrequency (%) 
134110.1%
 
115210.1%
 
111610.1%
 
86410.1%
 
81310.1%
 

TempAvg
Real number (ℝ≥0)

Distinct176
Distinct (%)22.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean67.78893179
Minimum4
Maximum920
Zeros0
Zeros (%)0.0%
Memory size6.1 KiB
2020-10-29T22:21:11.922153image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile12
Q133
median52
Q381
95-th percentile164.4
Maximum920
Range916
Interquartile range (IQR)48

Descriptive statistics

Standard deviation66.4597912
Coefficient of variation (CV)0.9803929557
Kurtosis42.48019408
Mean67.78893179
Median Absolute Deviation (MAD)21
Skewness4.856746574
Sum52672
Variance4416.903846
MonotocityNot monotonic
2020-10-29T22:21:12.079236image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
50172.2%
 
48151.9%
 
31141.8%
 
45141.8%
 
59131.7%
 
36131.7%
 
40121.5%
 
54121.5%
 
49121.5%
 
24121.5%
 
Other values (166)64382.8%
 
ValueCountFrequency (%) 
410.1%
 
560.8%
 
640.5%
 
760.8%
 
830.4%
 
ValueCountFrequency (%) 
92010.1%
 
49910.1%
 
46910.1%
 
42620.3%
 
38810.1%
 

SpatMax
Real number (ℝ≥0)

Distinct721
Distinct (%)92.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8935.294723
Minimum971
Maximum61411
Zeros0
Zeros (%)0.0%
Memory size6.1 KiB
2020-10-29T22:21:12.385968image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum971
5-th percentile1786.8
Q13961
median7067
Q312055
95-th percentile23169.4
Maximum61411
Range60440
Interquartile range (IQR)8094

Descriptive statistics

Standard deviation6903.52489
Coefficient of variation (CV)0.7726130031
Kurtosis6.563241964
Mean8935.294723
Median Absolute Deviation (MAD)3669
Skewness1.92414089
Sum6942724
Variance47658655.91
MonotocityNot monotonic
2020-10-29T22:21:12.528273image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
625840.5%
 
929340.5%
 
300030.4%
 
463830.4%
 
738030.4%
 
863420.3%
 
718520.3%
 
879720.3%
 
347520.3%
 
2298220.3%
 
Other values (711)75096.5%
 
ValueCountFrequency (%) 
97110.1%
 
100010.1%
 
108310.1%
 
108410.1%
 
113210.1%
 
ValueCountFrequency (%) 
6141110.1%
 
4425210.1%
 
4315610.1%
 
3826210.1%
 
3549210.1%
 

SpatAvg
Real number (ℝ≥0)

Distinct728
Distinct (%)93.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4135.042471
Minimum135
Maximum16851
Zeros0
Zeros (%)0.0%
Memory size6.1 KiB
2020-10-29T22:21:12.670755image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum135
5-th percentile1092.4
Q12092
median3565
Q35378
95-th percentile9771.8
Maximum16851
Range16716
Interquartile range (IQR)3286

Descriptive statistics

Standard deviation2705.941291
Coefficient of variation (CV)0.6543926236
Kurtosis2.290705964
Mean4135.042471
Median Absolute Deviation (MAD)1553
Skewness1.36847237
Sum3212928
Variance7322118.273
MonotocityNot monotonic
2020-10-29T22:21:12.810353image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
882240.5%
 
169830.4%
 
141330.4%
 
378120.3%
 
153320.3%
 
378620.3%
 
719120.3%
 
615720.3%
 
388620.3%
 
1657120.3%
 
Other values (718)75396.9%
 
ValueCountFrequency (%) 
13510.1%
 
38710.1%
 
45810.1%
 
47610.1%
 
55910.1%
 
ValueCountFrequency (%) 
1685110.1%
 
1657120.3%
 
1552610.1%
 
1415010.1%
 
1290710.1%
 

TempDist
Real number (ℝ≥0)

Distinct25
Distinct (%)3.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.956241956
Minimum0
Maximum24
Zeros2
Zeros (%)0.3%
Memory size6.1 KiB
2020-10-29T22:21:12.946338image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q15
median8
Q314
95-th percentile22
Maximum24
Range24
Interquartile range (IQR)9

Descriptive statistics

Standard deviation6.226658706
Coefficient of variation (CV)0.6254025096
Kurtosis-0.4599920532
Mean9.956241956
Median Absolute Deviation (MAD)4
Skewness0.7023348555
Sum7736
Variance38.77127864
MonotocityNot monotonic
2020-10-29T22:21:13.074174image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%) 
6719.1%
 
7648.2%
 
8557.1%
 
9536.8%
 
5526.7%
 
3486.2%
 
10486.2%
 
4435.5%
 
12364.6%
 
1334.2%
 
Other values (15)27435.3%
 
ValueCountFrequency (%) 
020.3%
 
1334.2%
 
2222.8%
 
3486.2%
 
4435.5%
 
ValueCountFrequency (%) 
24182.3%
 
23182.3%
 
22222.8%
 
21233.0%
 
20141.8%
 

SpatDist
Categorical

HIGH CORRELATION

Distinct4
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size6.1 KiB
0
774 
1519
 
1
44
 
1
18
 
1
ValueCountFrequency (%) 
077499.6%
 
151910.1%
 
4410.1%
 
1810.1%
 
2020-10-29T22:21:13.209800image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique3 ?
Unique (%)0.4%
2020-10-29T22:21:13.300436image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-29T22:21:13.424412image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length4
Median length1
Mean length1.006435006
Min length1

Coverage
Real number (ℝ≥0)

Distinct94
Distinct (%)12.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean52.51866152
Minimum5
Maximum100
Zeros0
Zeros (%)0.0%
Memory size6.1 KiB
2020-10-29T22:21:13.559510image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile20
Q136
median51
Q367
95-th percentile91
Maximum100
Range95
Interquartile range (IQR)31

Descriptive statistics

Standard deviation21.2014752
Coefficient of variation (CV)0.4036941268
Kurtosis-0.5742489635
Mean52.51866152
Median Absolute Deviation (MAD)15
Skewness0.2404176829
Sum40807
Variance449.5025508
MonotocityNot monotonic
2020-10-29T22:21:13.702746image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
44273.5%
 
42222.8%
 
59202.6%
 
36172.2%
 
53151.9%
 
60151.9%
 
37151.9%
 
39151.9%
 
55151.9%
 
45141.8%
 
Other values (84)60277.5%
 
ValueCountFrequency (%) 
510.1%
 
620.3%
 
720.3%
 
810.1%
 
910.1%
 
ValueCountFrequency (%) 
100121.5%
 
9830.4%
 
9720.3%
 
9640.5%
 
9530.4%
 

TempGL
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size6.1 KiB
1
775 
3
 
2
ValueCountFrequency (%) 
177599.7%
 
320.3%
 
2020-10-29T22:21:13.841681image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-10-29T22:21:13.923501image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-29T22:21:14.011244image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length1
Median length1
Mean length1
Min length1

SpatGL
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size6.1 KiB
2
774 
1
 
3
ValueCountFrequency (%) 
277499.6%
 
130.4%
 
2020-10-29T22:21:14.127610image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-10-29T22:21:14.208801image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-29T22:21:14.286327image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length1
Median length1
Mean length1
Min length1

TempIL
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size6.1 KiB
-1
775 
2
 
2
ValueCountFrequency (%) 
-177599.7%
 
220.3%
 
2020-10-29T22:21:14.401677image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-10-29T22:21:14.479950image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-29T22:21:14.562053image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length2
Median length2
Mean length1.997425997
Min length1

SpatIL
Real number (ℝ)

Distinct6
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.64993565
Minimum-1
Maximum5
Zeros0
Zeros (%)0.0%
Memory size6.1 KiB
2020-10-29T22:21:14.659525image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile2
Q13
median4
Q35
95-th percentile5
Maximum5
Range6
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.106903418
Coefficient of variation (CV)0.303266557
Kurtosis-0.02115667042
Mean3.64993565
Median Absolute Deviation (MAD)1
Skewness-0.5622119547
Sum2836
Variance1.225235176
MonotocityNot monotonic
2020-10-29T22:21:14.746779image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%) 
427034.7%
 
519925.6%
 
316120.7%
 
213717.6%
 
170.9%
 
-130.4%
 
ValueCountFrequency (%) 
-130.4%
 
170.9%
 
213717.6%
 
316120.7%
 
427034.7%
 
ValueCountFrequency (%) 
519925.6%
 
427034.7%
 
316120.7%
 
213717.6%
 
170.9%
 

TLCar
Real number (ℝ≥0)

Distinct530
Distinct (%)68.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1510.346203
Minimum1001
Maximum1999
Zeros0
Zeros (%)0.0%
Memory size6.1 KiB
2020-10-29T22:21:14.866509image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1001
5-th percentile1049.8
Q11256
median1525
Q31759
95-th percentile1942.4
Maximum1999
Range998
Interquartile range (IQR)503

Descriptive statistics

Standard deviation290.341165
Coefficient of variation (CV)0.1922348428
Kurtosis-1.223809578
Mean1510.346203
Median Absolute Deviation (MAD)250
Skewness-0.05773552798
Sum1173539
Variance84297.9921
MonotocityNot monotonic
2020-10-29T22:21:15.009350image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
190250.6%
 
142140.5%
 
158440.5%
 
122940.5%
 
186740.5%
 
189340.5%
 
119140.5%
 
156840.5%
 
199940.5%
 
169830.4%
 
Other values (520)73794.9%
 
ValueCountFrequency (%) 
100120.3%
 
100210.1%
 
100310.1%
 
100620.3%
 
101010.1%
 
ValueCountFrequency (%) 
199940.5%
 
199810.1%
 
199720.3%
 
199610.1%
 
199420.3%
 

TLHGV
Real number (ℝ≥0)

Distinct392
Distinct (%)50.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean746.8622909
Minimum500
Maximum999
Zeros0
Zeros (%)0.0%
Memory size6.1 KiB
2020-10-29T22:21:15.277219image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum500
5-th percentile524
Q1620
median742
Q3871
95-th percentile974
Maximum999
Range499
Interquartile range (IQR)251

Descriptive statistics

Standard deviation144.9885272
Coefficient of variation (CV)0.1941302018
Kurtosis-1.206602553
Mean746.8622909
Median Absolute Deviation (MAD)126
Skewness0.04740075698
Sum580312
Variance21021.67302
MonotocityNot monotonic
2020-10-29T22:21:15.419570image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
68060.8%
 
87160.8%
 
98260.8%
 
80760.8%
 
66450.6%
 
86950.6%
 
58850.6%
 
82250.6%
 
86250.6%
 
67250.6%
 
Other values (382)72393.1%
 
ValueCountFrequency (%) 
50030.4%
 
50120.3%
 
50410.1%
 
50520.3%
 
50650.6%
 
ValueCountFrequency (%) 
99920.3%
 
99810.1%
 
99620.3%
 
99520.3%
 
99420.3%
 

Strasse
Categorical

Distinct15
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Memory size6.1 KiB
A3
199 
A9
189 
A96
88 
A7
79 
A73
60 
Other values (10)
162 
ValueCountFrequency (%) 
A319925.6%
 
A918924.3%
 
A968811.3%
 
A77910.2%
 
A73607.7%
 
A6587.5%
 
A99303.9%
 
A92263.3%
 
A70212.7%
 
A94162.1%
 
Other values (5)111.4%
 
2020-10-29T22:21:15.559604image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique2 ?
Unique (%)0.3%
2020-10-29T22:21:15.682783image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length4
Median length2
Mean length2.325611326
Min length2

Kat
Categorical

Distinct4
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size6.1 KiB
3
423 
7
181 
2
144 
1
 
29
ValueCountFrequency (%) 
342354.4%
 
718123.3%
 
214418.5%
 
1293.7%
 
2020-10-29T22:21:15.816590image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-10-29T22:21:15.907929image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-29T22:21:16.016920image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length1
Median length1
Mean length1
Min length1

Typ
Real number (ℝ≥0)

Distinct6
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.81981982
Minimum1
Maximum7
Zeros0
Zeros (%)0.0%
Memory size6.1 KiB
2020-10-29T22:21:16.110501image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median6
Q36
95-th percentile7
Maximum7
Range6
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.197343398
Coefficient of variation (CV)0.4558974153
Kurtosis-0.7085507836
Mean4.81981982
Median Absolute Deviation (MAD)0
Skewness-1.058746496
Sum3745
Variance4.828318009
MonotocityNot monotonic
2020-10-29T22:21:16.195226image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%) 
649663.8%
 
118123.3%
 
7709.0%
 
3243.1%
 
440.5%
 
520.3%
 
ValueCountFrequency (%) 
118123.3%
 
3243.1%
 
440.5%
 
520.3%
 
649663.8%
 
ValueCountFrequency (%) 
7709.0%
 
649663.8%
 
520.3%
 
440.5%
 
3243.1%
 

Betei
Real number (ℝ≥0)

Distinct9
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.33976834
Minimum1
Maximum18
Zeros0
Zeros (%)0.0%
Memory size6.1 KiB
2020-10-29T22:21:16.294061image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median2
Q33
95-th percentile4
Maximum18
Range17
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.17770371
Coefficient of variation (CV)0.5033420147
Kurtosis41.88518952
Mean2.33976834
Median Absolute Deviation (MAD)0
Skewness4.029454519
Sum1818
Variance1.386986029
MonotocityNot monotonic
2020-10-29T22:21:16.385793image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%) 
239751.1%
 
316421.1%
 
113016.7%
 
4597.6%
 
5162.1%
 
740.5%
 
640.5%
 
820.3%
 
1810.1%
 
ValueCountFrequency (%) 
113016.7%
 
239751.1%
 
316421.1%
 
4597.6%
 
5162.1%
 
ValueCountFrequency (%) 
1810.1%
 
820.3%
 
740.5%
 
640.5%
 
5162.1%
 

UArt1
Real number (ℝ≥0)

ZEROS

Distinct10
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.867438867
Minimum0
Maximum9
Zeros24
Zeros (%)3.1%
Memory size6.1 KiB
2020-10-29T22:21:16.492252image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median2
Q37
95-th percentile9
Maximum9
Range9
Interquartile range (IQR)5

Descriptive statistics

Standard deviation2.809076371
Coefficient of variation (CV)0.726340213
Kurtosis-0.9095242367
Mean3.867438867
Median Absolute Deviation (MAD)1
Skewness0.8554085091
Sum3005
Variance7.890910056
MonotocityNot monotonic
2020-10-29T22:21:16.584014image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
234544.4%
 
314418.5%
 
810713.8%
 
98010.3%
 
1314.0%
 
0243.1%
 
7233.0%
 
5151.9%
 
640.5%
 
440.5%
 
ValueCountFrequency (%) 
0243.1%
 
1314.0%
 
234544.4%
 
314418.5%
 
440.5%
 
ValueCountFrequency (%) 
98010.3%
 
810713.8%
 
7233.0%
 
640.5%
 
5151.9%
 

UArt2
Real number (ℝ)

Distinct8
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.7773487773
Minimum-1
Maximum9
Zeros0
Zeros (%)0.0%
Memory size6.1 KiB
2020-10-29T22:21:16.679627image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile-1
Q1-1
median-1
Q3-1
95-th percentile9
Maximum9
Range10
Interquartile range (IQR)0

Descriptive statistics

Standard deviation3.618836983
Coefficient of variation (CV)4.655358171
Kurtosis0.8497271125
Mean0.7773487773
Median Absolute Deviation (MAD)0
Skewness1.650404466
Sum604
Variance13.09598111
MonotocityNot monotonic
2020-10-29T22:21:16.770998image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%) 
-161479.0%
 
97910.2%
 
8506.4%
 
3202.6%
 
260.8%
 
740.5%
 
130.4%
 
410.1%
 
ValueCountFrequency (%) 
-161479.0%
 
130.4%
 
260.8%
 
3202.6%
 
410.1%
 
ValueCountFrequency (%) 
97910.2%
 
8506.4%
 
740.5%
 
410.1%
 
3202.6%
 

AUrs1
Real number (ℝ≥0)

ZEROS

Distinct13
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.18018018
Minimum0
Maximum89
Zeros663
Zeros (%)85.3%
Memory size6.1 KiB
2020-10-29T22:21:16.870502image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile73
Maximum89
Range89
Interquartile range (IQR)0

Descriptive statistics

Standard deviation27.07737041
Coefficient of variation (CV)2.421908231
Kurtosis2.209209109
Mean11.18018018
Median Absolute Deviation (MAD)0
Skewness2.033416381
Sum8687
Variance733.1839881
MonotocityNot monotonic
2020-10-29T22:21:16.961681image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%) 
066385.3%
 
73638.1%
 
72202.6%
 
89111.4%
 
8250.6%
 
8840.5%
 
8630.4%
 
8130.4%
 
8710.1%
 
8410.1%
 
Other values (3)30.4%
 
ValueCountFrequency (%) 
066385.3%
 
72202.6%
 
73638.1%
 
7510.1%
 
7710.1%
 
ValueCountFrequency (%) 
89111.4%
 
8840.5%
 
8710.1%
 
8630.4%
 
8410.1%
 

AUrs2
Real number (ℝ≥0)

ZEROS

Distinct7
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.8108108108
Minimum0
Maximum89
Zeros769
Zeros (%)99.0%
Memory size6.1 KiB
2020-10-29T22:21:17.055833image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum89
Range89
Interquartile range (IQR)0

Descriptive statistics

Standard deviation7.973473191
Coefficient of variation (CV)9.833950269
Kurtosis94.64864091
Mean0.8108108108
Median Absolute Deviation (MAD)0
Skewness9.793242716
Sum630
Variance63.57627473
MonotocityNot monotonic
2020-10-29T22:21:17.144751image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%) 
076999.0%
 
7520.3%
 
7320.3%
 
8910.1%
 
8410.1%
 
8110.1%
 
8010.1%
 
ValueCountFrequency (%) 
076999.0%
 
7320.3%
 
7520.3%
 
8010.1%
 
8110.1%
 
ValueCountFrequency (%) 
8910.1%
 
8410.1%
 
8110.1%
 
8010.1%
 
7520.3%
 

AufHi
Real number (ℝ)

Distinct9
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.5456885457
Minimum-1
Maximum9
Zeros2
Zeros (%)0.3%
Memory size6.1 KiB
2020-10-29T22:21:17.243350image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile-1
Q1-1
median-1
Q33
95-th percentile4
Maximum9
Range10
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.095843966
Coefficient of variation (CV)3.840732928
Kurtosis-0.5824286874
Mean0.5456885457
Median Absolute Deviation (MAD)0
Skewness0.8189660673
Sum424
Variance4.392561929
MonotocityNot monotonic
2020-10-29T22:21:17.337564image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%) 
-149263.3%
 
324131.0%
 
4243.1%
 
5121.5%
 
920.3%
 
820.3%
 
020.3%
 
210.1%
 
110.1%
 
ValueCountFrequency (%) 
-149263.3%
 
020.3%
 
110.1%
 
210.1%
 
324131.0%
 
ValueCountFrequency (%) 
920.3%
 
820.3%
 
5121.5%
 
4243.1%
 
324131.0%
 

Alkoh
Categorical

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size6.1 KiB
-1
766 
1
 
11
ValueCountFrequency (%) 
-176698.6%
 
1111.4%
 
2020-10-29T22:21:17.446080image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-10-29T22:21:17.526162image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-29T22:21:17.612921image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length2
Median length2
Mean length1.985842986
Min length1

Char1
Real number (ℝ)

Distinct5
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.2805662806
Minimum-1
Maximum6
Zeros0
Zeros (%)0.0%
Memory size6.1 KiB
2020-10-29T22:21:17.712721image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile-1
Q1-1
median-1
Q3-1
95-th percentile5
Maximum6
Range7
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.934772873
Coefficient of variation (CV)-6.895956525
Kurtosis3.952850503
Mean-0.2805662806
Median Absolute Deviation (MAD)0
Skewness2.395433217
Sum-218
Variance3.743346071
MonotocityNot monotonic
2020-10-29T22:21:17.804222image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=5)
ValueCountFrequency (%) 
-168087.5%
 
5384.9%
 
4334.2%
 
6222.8%
 
240.5%
 
ValueCountFrequency (%) 
-168087.5%
 
240.5%
 
4334.2%
 
5384.9%
 
6222.8%
 
ValueCountFrequency (%) 
6222.8%
 
5384.9%
 
4334.2%
 
240.5%
 
-168087.5%
 

Char2
Categorical

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size6.1 KiB
-1
748 
6
 
29
ValueCountFrequency (%) 
-174896.3%
 
6293.7%
 
2020-10-29T22:21:17.921247image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-10-29T22:21:17.999558image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-29T22:21:18.081363image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length2
Median length2
Mean length1.962676963
Min length1

Bes1
Categorical

Distinct3
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size6.1 KiB
-1
669 
6
106 
1
 
2
ValueCountFrequency (%) 
-166986.1%
 
610613.6%
 
120.3%
 
2020-10-29T22:21:18.328015image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-10-29T22:21:18.414791image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-29T22:21:18.507395image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length2
Median length2
Mean length1.861003861
Min length1

Bes2
Categorical

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size6.1 KiB
-1
776 
6
 
1
ValueCountFrequency (%) 
-177699.9%
 
610.1%
 
2020-10-29T22:21:18.624063image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique1 ?
Unique (%)0.1%
2020-10-29T22:21:18.703829image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-29T22:21:18.786876image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length2
Median length2
Mean length1.998712999
Min length1

Lich1
Categorical

HIGH CORRELATION

Distinct4
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size6.1 KiB
0
582 
2
143 
1
 
51
-1
 
1
ValueCountFrequency (%) 
058274.9%
 
214318.4%
 
1516.6%
 
-110.1%
 
2020-10-29T22:21:18.904202image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique1 ?
Unique (%)0.1%
2020-10-29T22:21:18.980555image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-29T22:21:19.084081image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length2
Median length1
Mean length1.001287001
Min length1

Lich2
Categorical

HIGH CORRELATION

Distinct3
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size6.1 KiB
-1
583 
4
187 
3
 
7
ValueCountFrequency (%) 
-158375.0%
 
418724.1%
 
370.9%
 
2020-10-29T22:21:19.208410image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-10-29T22:21:19.289189image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-29T22:21:19.382085image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length2
Median length2
Mean length1.75032175
Min length1

Zust1
Categorical

Distinct4
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size6.1 KiB
0
549 
1
208 
2
 
18
-1
 
2
ValueCountFrequency (%) 
054970.7%
 
120826.8%
 
2182.3%
 
-120.3%
 
2020-10-29T22:21:19.499981image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-10-29T22:21:19.579831image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-29T22:21:19.684436image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length2
Median length1
Mean length1.002574003
Min length1

Zust2
Categorical

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size6.1 KiB
-1
770 
2
 
7
ValueCountFrequency (%) 
-177099.1%
 
270.9%
 
2020-10-29T22:21:19.808087image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-10-29T22:21:19.886458image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-29T22:21:19.970205image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length2
Median length2
Mean length1.990990991
Min length1

Fstf
Categorical

MISSING

Distinct7
Distinct (%)1.0%
Missing52
Missing (%)6.7%
Memory size6.1 KiB
2
316 
1
266 
3
119 
S
 
11
4
 
10
Other values (2)
 
3
ValueCountFrequency (%) 
231640.7%
 
126634.2%
 
311915.3%
 
S111.4%
 
4101.3%
 
520.3%
 
F10.1%
 
(Missing)526.7%
 
2020-10-29T22:21:20.092338image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique1 ?
Unique (%)0.1%
2020-10-29T22:21:20.180111image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-29T22:21:20.321319image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length3
Median length1
Mean length1.133848134
Min length1

WoTag
Categorical

Distinct8
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size6.1 KiB
Fr
130 
Mo
117 
Di
111 
Do
106 
Sa
104 
Other values (3)
209 
ValueCountFrequency (%) 
Fr13016.7%
 
Mo11715.1%
 
Di11114.3%
 
Do10613.6%
 
Sa10413.4%
 
Mi10313.3%
 
So9912.7%
 
70.9%
 
2020-10-29T22:21:20.448112image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-10-29T22:21:20.536397image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-29T22:21:20.708154image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length2
Median length2
Mean length1.981981982
Min length0

FeiTag
Categorical

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size6.1 KiB
-1
753 
1
 
24
ValueCountFrequency (%) 
-175396.9%
 
1243.1%
 
2020-10-29T22:21:20.840576image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-10-29T22:21:20.917797image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-29T22:21:20.999319image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length2
Median length2
Mean length1.969111969
Min length1

Month
Categorical

Distinct12
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Memory size6.1 KiB
Aug
92 
Jul
80 
Apr
71 
Jun
70 
May
67 
Other values (7)
397 
ValueCountFrequency (%) 
Aug9211.8%
 
Jul8010.3%
 
Apr719.1%
 
Jun709.0%
 
May678.6%
 
Oct668.5%
 
Dec658.4%
 
Mar648.2%
 
Sep587.5%
 
Nov577.3%
 
Other values (2)8711.2%
 
2020-10-29T22:21:21.116623image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-10-29T22:21:21.236338image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length3
Median length3
Mean length3
Min length3

Interactions

2020-10-29T22:20:10.692688image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-29T22:20:11.156979image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-29T22:20:11.625542image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-29T22:20:12.234568image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-29T22:20:12.703540image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-29T22:20:13.171874image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-29T22:20:13.641244image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-29T22:20:14.104754image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-29T22:20:14.570418image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-29T22:20:15.027915image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-29T22:20:15.489274image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-29T22:20:15.948353image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-29T22:20:16.407190image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-29T22:20:16.867733image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-29T22:20:17.330290image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-29T22:20:17.793128image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-29T22:20:18.258139image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-29T22:20:18.720696image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-29T22:20:20.076981image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-29T22:20:20.098635image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-29T22:20:20.255193image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-29T22:20:20.394863image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-29T22:20:20.548127image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-29T22:20:20.700242image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-29T22:20:20.848181image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-29T22:20:20.987326image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-29T22:20:21.569436image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-29T22:20:21.712667image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-29T22:20:21.859922image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-29T22:20:22.008688image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-29T22:20:22.889821image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-29T22:20:23.029270image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-29T22:20:23.176223image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-29T22:20:23.314464image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-29T22:20:23.455580image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-29T22:20:23.598158image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-29T22:20:24.236000image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
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2020-10-29T22:20:56.023386image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-29T22:20:56.154021image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-29T22:20:56.428267image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-29T22:20:56.558839image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-29T22:20:56.684202image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-29T22:20:56.813245image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-29T22:20:56.933006image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-29T22:20:57.049184image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-29T22:20:57.168969image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-29T22:20:57.290702image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-29T22:20:57.412941image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-29T22:20:58.012893image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-29T22:20:58.034124image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-29T22:20:58.167205image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-29T22:20:58.287874image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-29T22:20:58.416931image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-29T22:20:58.550396image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-29T22:20:58.674486image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-29T22:20:58.797109image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-29T22:20:58.926972image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-29T22:20:59.054624image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-29T22:20:59.183554image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-29T22:20:59.316485image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-29T22:20:59.448021image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-29T22:20:59.572798image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-29T22:20:59.692494image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-29T22:20:59.813435image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-29T22:20:59.934618image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-29T22:21:00.057806image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-29T22:21:00.658438image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-29T22:21:00.679477image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-29T22:21:00.815371image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-29T22:21:00.934882image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-29T22:21:01.064227image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-29T22:21:01.196810image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-29T22:21:01.324068image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-29T22:21:01.446515image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-29T22:21:01.730763image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-29T22:21:01.865180image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-29T22:21:01.991355image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-29T22:21:02.120191image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-29T22:21:02.247936image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-29T22:21:02.369244image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-29T22:21:02.491282image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-29T22:21:02.611819image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-29T22:21:02.729268image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-29T22:21:02.850821image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-29T22:21:03.435706image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-29T22:21:03.456843image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-29T22:21:03.592758image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-29T22:21:03.709569image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-29T22:21:03.855557image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-29T22:21:03.992297image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-29T22:21:04.115879image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-29T22:21:04.234852image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-29T22:21:04.363258image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-29T22:21:04.486830image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-29T22:21:04.614005image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-29T22:21:04.741060image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-29T22:21:04.870359image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-29T22:21:04.990570image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-29T22:21:05.109814image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-29T22:21:05.228226image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-29T22:21:05.348391image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-29T22:21:05.461400image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-29T22:21:06.062865image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-29T22:21:06.084127image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-29T22:21:06.232952image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-29T22:21:06.370333image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-29T22:21:06.515429image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-29T22:21:06.661495image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-29T22:21:06.803062image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-29T22:21:07.086151image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-29T22:21:07.233542image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-29T22:21:07.375868image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-29T22:21:07.516542image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-29T22:21:07.657114image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-29T22:21:07.803566image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-29T22:21:07.936668image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-29T22:21:08.075823image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-29T22:21:08.209818image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-29T22:21:08.351488image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-29T22:21:08.488028image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Correlations

2020-10-29T22:21:21.964559image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2020-10-29T22:21:22.489327image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2020-10-29T22:21:23.014665image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2020-10-29T22:21:23.552265image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2020-10-29T22:21:23.628304image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2020-10-29T22:21:09.570381image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-29T22:21:10.363556image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-29T22:21:10.916211image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Sample

First rows

df_indexTempMaxTempAvgSpatMaxSpatAvgTempDistSpatDistCoverageTempGLSpatGLTempILSpatILTLCarTLHGVStrasseKatTypBeteiUArt1UArt2AUrs1AUrs2AufHiAlkohChar1Char2Bes1Bes2Lich1Lich2Zust1Zust2FstfWoTagFeiTagMonth
016928600034751004112-151691686A63632-1890-1-1-1-1-1-10-10-12Di1Jan
17216961299156151104312-121647670A33733-100-1-1-1-1-1-1241-12Do-1Jan
2123315419314931203412-141079686A97119-17203-1-1-1-1-10-12-13Sa-1Jan
31613245963126322202512-121098683A97119-172733-1-1-1-1-10-1123Sa-1Jan
41718959181603654602012-131581554A963623-1720-1-1-1-16-10-12-12Sa-1Jan
5211085260772809104412-141942930A63622-100-1-1-1-1-1-1240-11Mo-1Jan
6221026859994222905912-151610680A737642-100-1-1-1-1-1-1241-12Mo-1Jan
7231026859994222905912-151610680A737632-100-1-1-1-1-1-1241-12Mo-1Jan
824392715048762305712-151362731A72118-17304-14-1-1-1141-12Di-1Jan
928181930002742308712-141639822A67623-100-1-1-1-1-1-1242-11Mi-1Jan

Last rows

df_indexTempMaxTempAvgSpatMaxSpatAvgTempDistSpatDistCoverageTempGLSpatGLTempILSpatILTLCarTLHGVStrasseKatTypBeteiUArt1UArt2AUrs1AUrs2AufHiAlkohChar1Char2Bes1Bes2Lich1Lich2Zust1Zust2FstfWoTagFeiTagMonth
76718511178862584606607412-151823744A33642-100-1-1-1-1-1-1240-12Do1Dec
76818532424100010901008812-141475636A93139-1003-156-1-10-11-13Do1Dec
76918558149308222941607112-131562890A37623200-1-1-1-1-1-1240-11Fr-1Dec
770185696641628210596306512-131688567A62622-100-1-1-1-1-1-10-10-12Fr-1Dec
7711857816126982360808612-141305511A73119-1003-16-1-1-1240-12Fr-1Dec
7721860201931499943721602912-121875579A73622-100-1-1-1-1-1-10-10-11Sa-1Dec
77318614526395527682207512-141262925A93622-100-1-1-1-1-1-10-10-11So-1Dec
7741862393113456100071107412-131850582A93734-100-1-1-1-1-1-1240-13So-1Dec
7751865937234182571407512-151950993A33632-100-1-1-1-1-1-1230-12-1Dec
7761866696214061260607812-141155500A712622-100-1-1-1-1-1-10-10-11Di-1Dec